Samples from implicit generative models are difficult to judge quantitatively: particularly for images, it is typically easy for humans to identify certain kinds of samples which are very unlikely under the reference distribution, but very difficult for humans to identify when modes are missing, or when types are merely under- or over-represented. This talk will overview different approaches towards evaluating the output of an implicit generative model, with a focus on identifying ways in which the model has failed. Some of these approaches also form the basis for the objective functions of GAN variants which can help avoid some of the issues of stability and mode-dropping in the original GAN.

Kerrie Mengerson, Probabilistic Modelling in the Real World

Interest is intensifying in the development and application of Bayesian approaches to estimation of real-world processes using probabilistic models. This presentation will focus on three substantive case studies in which we have been involved: protecting the Great Barrier Reef in Australia from impacts such as crown of thorns starfish and industrial dredging, reducing congestion at international airports, and predicting survival of jaguars in the Peruvian Amazon. Through these examples, we will explore current ideas about Approximate Bayesian Computation, Populations of Models, Bayesian priors and p-values, and Bayesian dynamic networks.

Sanjeev Arora, Do GANs actually learn the distribution? Some theory and empirics

The Generative Adversarial Nets or GANs framework (Goodfellow et al'14) for learning distributions differs from older ideas such as autoencoders and deep Boltzmann machines in that it scores the generated distribution using a discriminator net, instead of a perplexity-like calculation. It appears to work well in practice, e.g., the generated images look better than older techniques. But how well do these nets learn the target distribution?Our paper 1 (ICML'17) shows GAN training may not have good generalization properties; e.g., training may appear successful but the trained distribution may be far from target distribution in standard metrics. We show theoretically that this can happen even though the 2-person game between discriminator and generator is in near-equilibrium, where the generator appears to have "won" (with respect to natural training objectives).Paper2 (arxiv June 26) empirically tests whether this lack of generalization occurs in real-life training. The paper introduces a new quantitative test for diversity of a distribution based upon the famous birthday paradox. This test reveals that distributions learnt by some leading GANs techniques have fairly small support (i.e., suffer from mode collapse), which implies that they are far from the target distribution.Paper 1: "Equilibrium and Generalization in GANs" by Arora, Ge, Liang, Ma, Zhang. (ICML 2017)Paper 2: "Do GANs actually learn the distribution? An empirical study." by Arora and Zhang (https://arxiv.org/abs/1706.08224)

Stefano Ermon, Generative Adversarial Imitation Learning

Consider learning a policy from example expert behavior, without interaction with the expert or access to a reward or cost signal. One approach is to recover the expert’s cost function with inverse reinforcement learning, then compute an optimal policy for that cost function. This approach is indirect and can be slow. In this talk, I will discuss a new generative modeling framework for directly extracting a policy from data, drawing an analogy between imitation learning and generative adversarial networks. I will derive a model-free imitation learning algorithm that obtains significant performance gains over existing methods in imitating complex behaviors in large, high-dimensional environments. Our approach can also be used to infer the latent structure of human demonstrations in an unsupervised way. As an example, I will show a driving application where a model learned from demonstrations is able to both produce different driving styles and accurately anticipate human actions using raw visual inputs.

Qiang Liu

Wild Variational Inference with Expressive Variational Families

Variational inference (VI) provides a powerful tool for reasoning with highly complex probabilistic models in machine learning. The basic idea of VI is to approximate complex target distributions with simpler distributions found by minimizing the KL divergence within some predefined parametric families. A key limitation of the typical VI techniques, however, is that they require the variational family to be simple enough to have tractable likelihood functions, which excludes a broad range of flexible, expressive families such as these defined via implicit models. In this talk, we will discuss a general framework for (wild) variational inference that works for much more expressive, implicitly defined variational families with intractable likelihood functions. Our key idea is to first lift the optimization problem into the infinite dimensional space, solved using nonparametric particle methods, and then project the update back to the finite dimensional parameter space that we want to optimize with. Our framework is highly general and allows us to leverage any existing particle methods as the inference engine for wild variational inference, including MCMC and Stein variational gradient methods.